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QDCRNet:使用基因表达数据进行病毒检测的量子扩展卷积循环网络

S Karthi¹, T Ramalingam², R Iyswarya²

¹Department of IT, St Joseph College of Engineering/ Anna University, Sriperumbudur, Chennai, Tamil Nadu 602117, India.

Computational biology and chemistry

|August 21, 2025

概括

一个新的量子扩展卷积循环网络 (QDCRNet) 增强了基因表达数据的病毒检测. 这种先进的模型提高了诊断准确度, 有助于及时治疗和预防病毒感染.

科学领域:

生物信息学
计算生物学
基因组学

背景情况:

病毒感染对全球健康构成重大风险,需要准确和快速的检测方法.
目前的诊断挑战包括非特异性症状,可变的病毒表达和测试延迟.
有效的病毒鉴定对于及时治疗和控制疾病传播至关重要.

研究的目的:

利用基因表达数据开发和评估一种新型的深度学习模型,用于精确检测病毒.
通过先进的计算方法解决当前病毒诊断方法的局限性.

主要方法:

基因表达数据经历了Box-Cox转换.
使用Gower距离和相互信息进行特征选择,以确定相关的基因组区域.
使用量子扩展卷积循环网络 (QDCRNet) 实现了病毒检测,该网络集成了量子扩展卷积神经网络 (QDCNN) 和深度循环神经网络 (DRNN).

主要成果:

在病毒检测方面,QDCRNet模型表现出很高的性能.
获得了90.80%的准确性,90.50%的灵敏性和90.40%的特异性.

结论:

通过基因表达数据检测病毒,QDCRNet模型提供了一个强大而准确的解决方案.
这种方法有可能显著提高病毒感染的诊断速度和精度.

关键词:

盒子-转换基因表达相互提供信息量子扩展卷积神经网络 (QDCNN)病毒性疾病

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